My goal is to create a struct-like object in Django PostgreSQL, such as:
specs.coordinate.x
specs.coordinate.y
specs.coordinate.z
x , y , z should therefore be subclasses of coordinate. They should be me mutable as well, wherefore named tuples cannot be used.
I have tried it using the new dataclass:
from django.db import models
from dataclasses import dataclass
class Specs(models.Model):
name = models.CharField(max_length=80)
age = models.IntegerField()
@dataclass
class coordinate:
x: float
y: float
z: float
However the coordinate x,y,z items are not visible in pgAdmin:
What am I doing wrong? Is there any better approach for this than with dataclass?
If your goal is simply to use the syntax: specs.coordinate.x
in your datasheet you can simply use a OneToOne
Relation.
This also allows for the special case, that either all Coordinates values have to be set or none.
from django.db import models
class Coordinate(models.Model):
x = models.FloatField()
y = models.FloatField()
z = models.FloatField()
class Specs(models.Model):
name = models.CharField(max_length=80)
age = models.IntegerField()
coordinate = models.OneToOneField(
Coordinate,
null=True,
on_delete=models.SET_NULL
)
Now you can use Specs just like the a Dataclass.
This solution uses dacite library to achieve support to nested dataclasses.
from dacite import from_dict
from django.db import models
from dataclasses import dataclass, asdict
import json
"""Field that maps dataclass to django model fields."""
class DataClassField(models.CharField):
description = "Map python dataclasses to model."
def __init__(self, dataClass, *args, **kwargs):
self.dataClass = dataClass
if not kwargs['max_length']
kwargs['max_length'] = 1024
super().__init__(*args, **kwargs)
def deconstruct(self):
name, path, args, kwargs = super().deconstruct()
kwargs['dataClass'] = self.dataClass
return name, path, args, kwargs
def from_db_value(self, value, expression, connection):
if value is None:
return value
obj = json.loads(value)
return from_dict(data_class=self.dataClass, data=obj)
def to_python(self, value):
if isinstance(value, self.dataClass):
return value
if value is None:
return value
obj = json.loads(value)
return from_dict(data_class=self.dataClass, data=obj)
def get_prep_value(self, value):
if value is None:
return value
return json.dumps(asdict(value)
@dataclass
class MyDataclass1:
foo: str
@dataclass
class MyDataclass2:
bar: MyDataclass1
class MyModel(models.Model)
dc1 = DataClassField(dataClass=MyDataclass1, null=True)
dc2 = DataClassField(dataClass=MyDataclass2, null=True)
instance = MyModel(dc1=MyDataclass1(foo="foo"))
instance.save()
instance.dc1.foo # Should return "foo"
instance.dc1 # Should return <MyDataclass1 .... at ....>
To add support for custom datatypes (for example datetimes) you need to specify JSON decoding and encoding for these types as represented below:
def decode_datetime(dt: datetime) -> str:
return dt.strftime('%Y-%m-%dT%H:%M:%S%z')
def encode_datetime(dt: str) -> datetime:
return datetime.strptime(dt, '%Y-%m-%dT%H:%M:%S%z')
def default(o):
if isinstance(o, (datetime.datetime)):
return decode_datetime(o)
else:
print(o)
class DataclassField(models.CharField):
...
def from_db_value(self, value, expression, connection):
...
obj = json.loads(value)
return from_dict(data_class=self.data_class, data=obj,
config=Config(type_hooks={datetime.datetime: encode_datetime}))
def to_python(self, value):
...
return from_dict(data_class=self.data_class, data=obj,
config=Config(type_hooks={datetime.datetime: encode_datetime}))
def get_prep_value(self, value):
...
return json.dumps(asdict(value), sort_keys=True, indent=1, default=default)
I would use django's built-in PointField . It stores Point objects.
so you would use
from django.contrib.gis.db import models
class Specs(models.Model):
coordinate = models.PointField
In django you could access it as you would with a normal django model field:
spec.coordinate
And since is a Point
object, you could also do:
spec.coordinate.x
spec.coordinate.y
spec.coordinate.z
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